Fengjun Xiao
Papers
1
Total Citations
3
H-Index
1
About
Fengjun Xiao is a researcher whose work sits at the intersection of wireless communications and autonomous robotics, with a particular focus on the critical role of modulation recognition in enabling efficient robot swarm coordination. His most-cited paper, "Performance Evaluation of Machine Learning in Wireless Connected Robotics Swarms" (2019), systematically investigates how different classifiers can optimize signal transmission and negotiation within robot collectives. By evaluating the trade-offs between classification accuracy and communication reliability, Xiao provides foundational insights for designing more robust, self-organizing robotic networks. This work, which has garnered 3 citations, addresses a key bottleneck in swarm robotics: ensuring seamless data exchange despite diverse and noisy wireless environments. Xiao’s contributions are particularly valuable for researchers developing machine learning-driven communication protocols for autonomous systems, from industrial automation to search-and-rescue missions. His research underscores the importance of selecting appropriate classifiers to enhance both transmission efficiency and swarm resilience, offering a practical framework for advancing connected robotics in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1